Changing Crop Variety Could Extend Biopesticide Effectiveness

May 29, 2025

Changes to a pest's diet could slow the evolution of resistance to biopesticides, according to research from University of Stirling scientists.

It is hoped that the findings could allow the development of biopesticides that are effective for longer, potentially increasing food security, reducing damage to the natural environment and boosting agro-ecological biodiversity.

Researchers discovered that cotton bollworm pests—a species of moth that can cause considerable agricultural damage—show a great deal of genetic variation in how well they survive after being exposed to biopesticide fungi, which are often considered safer alternatives to chemical pesticides.

The study showed that exposure to biopesticide fungi might lead to the evolution of resistance, just as with synthetic pesticides, and builds on previous findings that indicated new approaches are required in managing resistance risks to greener pesticides.

However, alterations to the pest's diet, the crop it eats, had a greater impact on evolution of resistance than switching the type of pesticide used—meaning that the variety of crop grown could impact how quickly pests adapt to biopesticides.

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Video: Seeing the Whole Season: How Continuous Crop Modeling Is Changing Breeding

Plant breeding has long been shaped by snapshots. A walk through a plot. A single set of notes. A yield check at the end of the season. But crops do not grow in moments. They change every day.

In this conversation, Gary Nijak of AerialPLOT explains how continuous crop modeling is changing the way breeders see, measure, and select plants by capturing growth, stress, and recovery across the entire season, not just at isolated points in time.

Nijak breaks down why point-in-time observations can miss critical performance signals, how repeated, season-long data collection removes the human bottleneck in breeding, and what becomes possible when every plot is treated as a living data set. He also explores how continuous modeling allows breeding programs to move beyond vague descriptors and toward measurable, repeatable insights that connect directly to on-farm outcomes.

This conversation explores:

• What continuous crop modeling is and how it works

• Why traditional field observations fall short over a full growing season

• How scale and repeated measurement change breeding decisions

• What “digital twins” of plots mean for selection and performance

• Why data, not hardware, is driving the next shift in breeding innovation As data-driven breeding moves from research into real-world programs, this discussion offers a clear look at how seeing the whole season is reshaping value for breeders, seed companies, and farmers, and why this may be only the beginning.